The Residual is defined as the difference between the actual and predicted, or fitted values of the response variable. This statement is true.

Linear regression is a statistical tool that determines how well a straight line fits a set of paired data. The straight line that best fits that data is called the least squares regression line. This line can be used in a number of ways. One of these uses is to estimate the value of a response variable for a given value of an explanatory variable. Related to this idea is that of a residual.

Residuals are obtained by performing subtraction. All that we must do is to subtract the predicted value of y from the observed value of y for a particular x. The result is called a residual.

The formula for residuals is straightforward:

It is important to note that the predicted value comes from our regression line. The observed value comes from our data set.

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